Search and Rescue (SaR) operations rely on civilian volunteers to provide mandatory manpower when covering large areas of land. These SaR operations are typically conducted in remote environments where conditions can be hazardous and GPS signal intermittent. This project designed and tested an autonomous Unmanned Ground Vehicle (UGV) capable of sequentially navigating along a series of GPS waypoints in an effort to minimize harm to operators and bolster the ranks of SaR crews. An embedded controller was used to calculate vector trajectory for navigation and pathing corrections from data provided by an onboard GPS unit and compass. Scans from a 2D-LIDAR were processed and analyzed by an onboard computer before passing network-published shared variables to the embedded controller for obstacle detection and avoidance systems. A linear model dictated a predictive algorithm for navigating the GPS waypoints while GPS signal was unavailable. For testing, the UGV autonomously navigated within range of each waypoint before advancing to the next until all waypoints were met. For testing with intermittent GPS, the UGV autonomously navigated the same waypoints, but an additional system added randomly generated periods where the GPS was masked from the UGV's primary navigation system. This study results in a highly successful system of autonomous vehicle navigation and obstacle avoidance using vector trajectory but a limited system of navigating without GPS. An outline for future research in further improving the tested system is discussed.